Research Cell: An International Journal of Engineering Sciences

Sanjay Jamwal ¹, Devanand Padha ²

Affiliations
1 Dept of Computer Science & IT, Baba Ghula Shah Badshah University, Jammu, IN
2 Dept of Computer science & IT, University of Jammu, Jammu, IN

Abstract

In order to transform rural community into an information driven, modern and competitive society, the role of Information Communication Technology (ICT) can not be ruled out. The existing Transfer of Technology mechanisms and extension programs, mostly run by the government departments, eg: Community Information centers (CIC) by National Informatics Center (NIC) in Jammu and Kashmir state & North eastern states, Agriculture Universities, Agriculture knowledge centers (Known as Krishi vigyan kendras (KVK)) of India are slow and in many cases ineffective as bridges between the research community and the farmers as the proper use of ICT hasn't been made till date. For this study, a Stratified sampling has been done for the in-depth analysis to find out the relation of ICT with agriculture and rural development on a five point likert scale and using a Socio-Economic-Scale named Uday Pareek scale (Uday Pareek, 2001).

Keywords

ICT, Agriculture, Rural Development, Technology Divide, Farming Community, Community Information Center, Agriculture Knowledge Center, Krishi Vigyan Kendra.

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Rohini Mahajan ¹, Devanand Padha ²

Affiliations
1 Department of Computer Science, Central University of Jammu, Jammu, IN
2 Department Computer Science, Central University of Jammu, Jammu, IN

Abstract

Nowadays, the Facial Expression Recognition (FER) system is a very important research topic in the fields of pattern recognition, Human-Computer Interaction (HCI) and Artificial Intelligence. The most essential steps in FER system are facial feature extraction and classification of expressions which improves the system performance for human computer interaction. This article reviews the feature extraction approaches such as Local Binary Pattern (LBP) (Divide the examined window into cells (e.g. 16x16 pixels for each cell).For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, left-middle, left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counter-clockwise. Where the center pixel's value is greater than the neighbor's value, write "0". Otherwise, write "1". This gives an 8-digit binary number (which is usually converted to decimal for convenience).Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e., each combination of which pixels are smaller and which are greater than the center). This histogram can be seen as a 256-dimensional feature vector. Optionally normalize the histogram. Concatenate (normalized) histograms of all cells. This gives a feature vector for the entire window.), Spatio-Temporal feature using Local Zernike moment and Local Directional Position Pattern (LDPP). Also, the various combinations of Convolutional Neural Network (CNN) and Deep Neural Network (DNN) classifiers are reviewed. In addition, this paper makes a comparative study of various techniques used for analysing gesture based facial expressions and their corresponding performance.

Keywords

Facial Expression Recognition (FER), Convolutional Neural Network (CNN), Deep Neural Network (DNN), Local Binary Pattern (LBP), Feature Extraction.

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